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Machine Learning with Python(audit) [Resources](https://drive.google.com/drive/folders/1VKe2otKaAREkaCvmVG8FZm4oK0JRr4CZ?usp=sharing)   \nWhat all i learnt?    \n-  In this audit course, i have implemented the supervised and unsupervised learning algorithms\n-  Tuning the hyper parameters\n## 2. Machine Learning Foundation    \n### WEEK 1 | 20 July  [Resources](https://drive.google.com/drive/folders/196p39Nz6ECY0MesNwV8_3WMaWq33tYEd?usp=sharing)   \n-  Week 1 offers the basic intoduction about Machine learning, how it evolved\n-  Introduction to turicreate, SFrame and its basic implementation\n-  Solved quiz questions    \nNote: Check out the Resources to access .ipynb, data files and other materials. \n### WEEK 2 | 21 July | Use Case 1 [Resources](https://drive.google.com/drive/folders/1Okl0w3M7IFnBX7RA4W5fa38YdPATshcz?usp=sharing)   \nWhat all i learnt?\n-  Linear Regression use case approach and its other applications\n-  How to load .sframe data file\n-  Data exploration using turicreate.SFrame\n-  Train test split of SFrame data file\n-  Creating simple regression model using one/set of independent varibales\n-  Training the model, and evaluating it on test_data\n-  solved quiz questions    \nNote: Check out the Resources to access .ipynb, data files and other materials.   \n### WEEK 3 | 26 July | Use Case 2 [Resources](https://drive.google.com/drive/folders/1FSwDbLdF_ReJD26UojnRKquRrTnM3oc3?usp=sharing)   \nWhat all i learnt?\n-  linear Classifier (binary classificatio)\n\n# Deep Learning Specialization\n## 1. Neural Networks and Deep learning   \n### WEEK 1 | 27 July  [Resources](https://drive.google.com/drive/folders/1xAjxhIZRBQCWW6jQMhnpR6wfP6qVCy_k?usp=sharing)   \nWhat all i learnt? \n-  In this week we have introduction to neural networks and its examples    \n-  Check the hand written notes for more information    \n### WEEK 2 | 27 July  [Resources](https://drive.google.com/drive/folders/1xAjxhIZRBQCWW6jQMhnpR6wfP6qVCy_k?usp=sharing)   \nWhat all i learnt?    \n-  Logistic regression (binary classification)\n-  Gradient Descent in Logistic Regression, Cost Funtion\n-  Vectorization    \n\n### WEEK 3 | 1 August [Resources](https://drive.google.com/drive/folders/1xAjxhIZRBQCWW6jQMhnpR6wfP6qVCy_k?usp=sharing)   \nWhat all i learnt?\n-  Forward Propagation\n-  Backward Propagation\n-  Gardients and updating the weights and bias\n-  single hidden layer neural network   \n### WEEK 4 | 5 August [Resources](https://drive.google.com/drive/folders/1xAjxhIZRBQCWW6jQMhnpR6wfP6qVCy_k?usp=sharing)   \nWhat all i learnt?\n-  L layered Neural Network\n-  Forward and Back Propagations\n-  Gardients and updating the weights and bias \n-  Implementing L layer neural network for a Simple Classification Problem (Cat vs no-Cat)    \n## 2. Improving Deep Neural Networks (Hyperparameter tuning, Regularization and Optimization)  \n### WEEK 1 | 10 August  [Resources](https://drive.google.com/drive/folders/1A6ywFEvLgzjdp0XCVBmHSUogFiZMP_B-?usp=sharing)   \nWhat all i learnt?\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadicherlavenkatasai%2Fcoursera","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadicherlavenkatasai%2Fcoursera","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadicherlavenkatasai%2Fcoursera/lists"}